Biostat 203B Homework 2

Due Feb 8 Feb 11, 2026 @ 11:59PM

Display machine information for reproducibility:

sessionInfo()

R version 4.5.2 (2025-10-31)
Platform: aarch64-apple-darwin20
Running under: macOS Tahoe 26.2

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Los_Angeles
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] htmlwidgets_1.6.4 compiler_4.5.2    fastmap_1.2.0     cli_3.6.5        
 [5] tools_4.5.2       htmltools_0.5.9   otel_0.2.0        rstudioapi_0.17.1
 [9] yaml_2.3.12       rmarkdown_2.30    knitr_1.51        jsonlite_2.0.0   
[13] xfun_0.55         digest_0.6.39     rlang_1.1.7       evaluate_1.0.5   

Load necessary libraries (you can add more as needed).

library(arrow)

Attaching package: 'arrow'

The following object is masked from 'package:utils':

    timestamp

library(data.table)
library(duckdb)

Loading required package: DBI

library(memuse)
library(pryr)

Attaching package: 'pryr'

The following object is masked from 'package:data.table':

    address

library(R.utils)

Loading required package: R.oo

Loading required package: R.methodsS3

R.methodsS3 v1.8.2 (2022-06-13 22:00:14 UTC) successfully loaded. See ?R.methodsS3 for help.

R.oo v1.27.1 (2025-05-02 21:00:05 UTC) successfully loaded. See ?R.oo for help.

Attaching package: 'R.oo'

The following object is masked from 'package:R.methodsS3':

    throw

The following objects are masked from 'package:methods':

    getClasses, getMethods

The following objects are masked from 'package:base':

    attach, detach, load, save

R.utils v2.13.0 (2025-02-24 21:20:02 UTC) successfully loaded. See ?R.utils for help.

Attaching package: 'R.utils'

The following object is masked from 'package:arrow':

    timestamp

The following object is masked from 'package:utils':

    timestamp

The following objects are masked from 'package:base':

    cat, commandArgs, getOption, isOpen, nullfile, parse, use, warnings

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.6
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.1     ✔ tibble    3.3.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.2
✔ purrr     1.2.1     

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::between()      masks data.table::between()
✖ purrr::compose()      masks pryr::compose()
✖ lubridate::duration() masks arrow::duration()
✖ tidyr::extract()      masks R.utils::extract()
✖ dplyr::filter()       masks stats::filter()
✖ dplyr::first()        masks data.table::first()
✖ lubridate::hour()     masks data.table::hour()
✖ lubridate::isoweek()  masks data.table::isoweek()
✖ lubridate::isoyear()  masks data.table::isoyear()
✖ dplyr::lag()          masks stats::lag()
✖ dplyr::last()         masks data.table::last()
✖ lubridate::mday()     masks data.table::mday()
✖ lubridate::minute()   masks data.table::minute()
✖ lubridate::month()    masks data.table::month()
✖ purrr::partial()      masks pryr::partial()
✖ lubridate::quarter()  masks data.table::quarter()
✖ lubridate::second()   masks data.table::second()
✖ purrr::transpose()    masks data.table::transpose()
✖ lubridate::wday()     masks data.table::wday()
✖ lubridate::week()     masks data.table::week()
✖ dplyr::where()        masks pryr::where()
✖ lubridate::yday()     masks data.table::yday()
✖ lubridate::year()     masks data.table::year()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Display memory information of your computer

memuse::Sys.meminfo()

Totalram:  96.000 GiB 
Freeram:    5.430 GiB 

In this exercise, we explore various tools for ingesting the MIMIC-IV data introduced in homework 1.

Display the contents of MIMIC hosp and icu data folders:

ls -l ~/mimic/hosp/

total 12306256
-rw-r--r--  1 huazhou  staff    19928140 Jun 24  2024 admissions.csv.gz
-rw-r--r--  1 huazhou  staff      427554 Apr 12  2024 d_hcpcs.csv.gz
-rw-r--r--  1 huazhou  staff      876360 Apr 12  2024 d_icd_diagnoses.csv.gz
-rw-r--r--  1 huazhou  staff      589186 Apr 12  2024 d_icd_procedures.csv.gz
-rw-r--r--  1 huazhou  staff       13169 Oct  3  2024 d_labitems.csv.gz
-rw-r--r--  1 huazhou  staff    33564802 Oct  3  2024 diagnoses_icd.csv.gz
-rw-r--r--  1 huazhou  staff     9743908 Oct  3  2024 drgcodes.csv.gz
-rw-r--r--  1 huazhou  staff   748158322 Apr 12  2024 emar_detail.csv.gz
-rw-r--r--  1 huazhou  staff   811305629 Apr 12  2024 emar.csv.gz
-rw-r--r--  1 huazhou  staff     2162335 Apr 12  2024 hcpcsevents.csv.gz
-rw-r--r--@ 1 huazhou  staff        2907 Dec 28  2024 index.html
-rw-r--r--  1 huazhou  staff  2592909134 Feb 20  2025 labevents.csv.gz
-rw-r--r--  1 huazhou  staff   117644075 Oct  3  2024 microbiologyevents.csv.gz
-rw-r--r--  1 huazhou  staff    44069351 Oct  3  2024 omr.csv.gz
-rw-r--r--  1 huazhou  staff     2835586 Apr 12  2024 patients.csv.gz
-rw-r--r--  1 huazhou  staff   525708076 Apr 12  2024 pharmacy.csv.gz
-rw-r--r--  1 huazhou  staff    55267894 Apr 12  2024 poe_detail.csv.gz
-rw-r--r--  1 huazhou  staff   666594177 Apr 12  2024 poe.csv.gz
-rw-r--r--  1 huazhou  staff   606298611 Apr 12  2024 prescriptions.csv.gz
-rw-r--r--  1 huazhou  staff     7777324 Apr 12  2024 procedures_icd.csv.gz
-rw-r--r--  1 huazhou  staff      127330 Apr 12  2024 provider.csv.gz
-rw-r--r--  1 huazhou  staff     8569241 Apr 12  2024 services.csv.gz
-rw-r--r--  1 huazhou  staff    46185771 Oct  3  2024 transfers.csv.gz

ls -l ~/mimic/icu/

total 8506792
-rw-r--r--  1 huazhou  staff       41566 Apr 12  2024 caregiver.csv.gz
-rw-r--r--  1 huazhou  staff  3502392765 Apr 12  2024 chartevents.csv.gz
-rw-r--r--  1 huazhou  staff       58741 Apr 12  2024 d_items.csv.gz
-rw-r--r--  1 huazhou  staff    63481196 Apr 12  2024 datetimeevents.csv.gz
-rw-r--r--  1 huazhou  staff     3342355 Oct  3  2024 icustays.csv.gz
-rw-r--r--  1 huazhou  staff        1336 Dec 28  2024 index.html
-rw-r--r--  1 huazhou  staff   311642048 Apr 12  2024 ingredientevents.csv.gz
-rw-r--r--  1 huazhou  staff   401088206 Apr 12  2024 inputevents.csv.gz
-rw-r--r--  1 huazhou  staff    49307639 Apr 12  2024 outputevents.csv.gz
-rw-r--r--  1 huazhou  staff    24096834 Apr 12  2024 procedureevents.csv.gz

Q1. read.csv (base R) vs read_csv (tidyverse) vs fread (data.table)

Q1.1 Speed, memory, and data types

There are quite a few utilities in R for reading plain text data files. Let us test the speed of reading a moderate sized compressed csv file, admissions.csv.gz, by three functions: read.csv in base R, read_csv in tidyverse, and fread in the data.table package.

Which function is fastest? Is there difference in the (default) parsed data types? How much memory does each resultant dataframe or tibble use? (Hint: system.time measures run times; pryr::object_size measures memory usage; all these readers can take gz file as input without explicit decompression.)

Q1.2 User-supplied data types

Re-ingest admissions.csv.gz by indicating appropriate column data types in read_csv. Does the run time change? How much memory does the result tibble use? (Hint: col_types argument in read_csv; result tibble should be less than 50MB.)

Q2. Ingest big data files

Let us focus on a bigger file, labevents.csv.gz, which is about 130x bigger than admissions.csv.gz.

ls -l ~/mimic/hosp/labevents.csv.gz

-rw-r--r--  1 huazhou  staff  2592909134 Feb 20  2025 /Users/huazhou/mimic/hosp/labevents.csv.gz

Display the first 10 lines of this file.

zcat < ~/mimic/hosp/labevents.csv.gz | head -10

labevent_id,subject_id,hadm_id,specimen_id,itemid,order_provider_id,charttime,storetime,value,valuenum,valueuom,ref_range_lower,ref_range_upper,flag,priority,comments
1,10000032,,2704548,50931,P69FQC,2180-03-23 11:51:00,2180-03-23 15:56:00,___,95,mg/dL,70,100,,ROUTINE,"IF FASTING, 70-100 NORMAL, >125 PROVISIONAL DIABETES."
2,10000032,,36092842,51071,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,
3,10000032,,36092842,51074,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,
4,10000032,,36092842,51075,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,"BENZODIAZEPINE IMMUNOASSAY SCREEN DOES NOT DETECT SOME DRUGS,;INCLUDING LORAZEPAM, CLONAZEPAM, AND FLUNITRAZEPAM."
5,10000032,,36092842,51079,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,
6,10000032,,36092842,51087,P69FQC,2180-03-23 11:51:00,,,,,,,,ROUTINE,RANDOM.
7,10000032,,36092842,51089,P69FQC,2180-03-23 11:51:00,2180-03-23 16:15:00,,,,,,,ROUTINE,PRESUMPTIVELY POSITIVE.
8,10000032,,36092842,51090,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,METHADONE ASSAY DETECTS ONLY METHADONE (NOT OTHER OPIATES/OPIOIDS).
9,10000032,,36092842,51092,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,"OPIATE IMMUNOASSAY SCREEN DOES NOT DETECT SYNTHETIC OPIOIDS;SUCH AS METHADONE, OXYCODONE, FENTANYL, BUPRENORPHINE, TRAMADOL,;NALOXONE, MEPERIDINE.  SEE ONLINE LAB MANUAL FOR DETAILS."

Q2.1 Ingest labevents.csv.gz by read_csv

Try to ingest labevents.csv.gz using read_csv. What happens? If it takes more than 3 minutes on your computer, then abort the program and report your findings.

Q2.2 Ingest selected columns of labevents.csv.gz by read_csv

Try to ingest only columns subject_id, itemid, charttime, and valuenum in labevents.csv.gz using read_csv. Does this solve the ingestion issue? (Hint: col_select argument in read_csv.)

Q2.3 Ingest a subset of labevents.csv.gz

Our first strategy to handle this big data file is to make a subset of the labevents data. Read the MIMIC documentation for the content in data file labevents.csv.

In later exercises, we will only be interested in the following lab items: albumin (50862), creatinine (50912), potassium (50971), sodium (50983), chloride (50902), bicarbonate (50882), hematocrit (51221), white blood cell count (51301), and glucose (50931) and the following columns: subject_id, itemid, charttime, valuenum. Write a Bash command to extract these columns and rows from labevents.csv.gz and save the result to a new file labevents_filtered.csv.gz in the current working directory. (Hint: Use zcat < to pipe the output of labevents.csv.gz to awk and then to gzip to compress the output. Do not put labevents_filtered.csv.gz in Git! To save render time, you can put #| eval: false at the beginning of this code chunk. TA will change it to #| eval: true before rendering your qmd file.)

Display the first 10 lines of the new file labevents_filtered.csv.gz. How many lines are in this new file, excluding the header? How long does it take read_csv to ingest labevents_filtered.csv.gz?

Q2.4 Ingest labevents.csv by Apache Arrow

Our second strategy is to use Apache Arrow for larger-than-memory data analytics. Unfortunately Arrow does not work with gz files directly. First decompress labevents.csv.gz to labevents.csv and put it in the current working directory (do not add it in Git!). To save render time, put #| eval: false at the beginning of this code chunk. TA will change it to #| eval: true when rendering your qmd file.

Then use arrow::open_dataset to ingest labevents.csv, select columns, and filter itemid as in Q2.3. How long does the ingest+select+filter process take? Display the number of rows and the first 10 rows of the result tibble, and make sure they match those in Q2.3. (Hint: use dplyr verbs for selecting columns and filtering rows.)

Write a few sentences to explain what Apache Arrow is. Imagine you want to explain it to a layman in an elevator.

Q2.5 Compress labevents.csv to Parquet format and ingest/select/filter

Re-write the csv file labevents.csv in the binary Parquet format (Hint: arrow::write_dataset.) How large is the Parquet file(s)? How long does the ingest+select+filter process of the Parquet file(s) take? Display the number of rows and the first 10 rows of the result tibble and make sure they match those in Q2.3. (Hint: use dplyr verbs for selecting columns and filtering rows.)

Write a few sentences to explain what the Parquet format is. Imagine you want to explain it to a layman in an elevator.

Q2.6 DuckDB

Ingest the Parquet file, convert it to a DuckDB table by arrow::to_duckdb, select columns, and filter rows as in Q2.5. How long does the ingest+convert+select+filter process take? Display the number of rows and the first 10 rows of the result tibble and make sure they match those in Q2.3. (Hint: use dplyr verbs for selecting columns and filtering rows.)

Write a few sentences to explain what DuckDB is. Imagine you want to explain it to a layman in an elevator.

Q3. Ingest and filter chartevents.csv.gz

chartevents.csv.gz contains all the charted data available for a patient. During their ICU stay, the primary repository of a patient’s information is their electronic chart. The itemid variable indicates a single measurement type in the database. The value variable is the value measured for itemid. The first 10 lines of chartevents.csv.gz are

zcat < ~/mimic/icu/chartevents.csv.gz | head -10

subject_id,hadm_id,stay_id,caregiver_id,charttime,storetime,itemid,value,valuenum,valueuom,warning
10000032,29079034,39553978,18704,2180-07-23 12:36:00,2180-07-23 14:45:00,226512,39.4,39.4,kg,0
10000032,29079034,39553978,18704,2180-07-23 12:36:00,2180-07-23 14:45:00,226707,60,60,Inch,0
10000032,29079034,39553978,18704,2180-07-23 12:36:00,2180-07-23 14:45:00,226730,152,152,cm,0
10000032,29079034,39553978,18704,2180-07-23 14:00:00,2180-07-23 14:18:00,220048,SR (Sinus Rhythm),,,0
10000032,29079034,39553978,18704,2180-07-23 14:00:00,2180-07-23 14:18:00,224642,Oral,,,0
10000032,29079034,39553978,18704,2180-07-23 14:00:00,2180-07-23 14:18:00,224650,None,,,0
10000032,29079034,39553978,18704,2180-07-23 14:00:00,2180-07-23 14:20:00,223761,98.7,98.7,°F,0
10000032,29079034,39553978,18704,2180-07-23 14:11:00,2180-07-23 14:17:00,220179,84,84,mmHg,0
10000032,29079034,39553978,18704,2180-07-23 14:11:00,2180-07-23 14:17:00,220180,48,48,mmHg,0

How many rows? 433 millions.

zcat < ~/mimic/icu/chartevents.csv.gz | tail -n +2 | wc -l

d_items.csv.gz is the dictionary for the itemid in chartevents.csv.gz.

zcat < ~/mimic/icu/d_items.csv.gz | head -10

itemid,label,abbreviation,linksto,category,unitname,param_type,lownormalvalue,highnormalvalue
220001,Problem List,Problem List,chartevents,General,,Text,,
220003,ICU Admission date,ICU Admission date,datetimeevents,ADT,,Date and time,,
220045,Heart Rate,HR,chartevents,Routine Vital Signs,bpm,Numeric,,
220046,Heart rate Alarm - High,HR Alarm - High,chartevents,Alarms,bpm,Numeric,,
220047,Heart Rate Alarm - Low,HR Alarm - Low,chartevents,Alarms,bpm,Numeric,,
220048,Heart Rhythm,Heart Rhythm,chartevents,Routine Vital Signs,,Text,,
220050,Arterial Blood Pressure systolic,ABPs,chartevents,Routine Vital Signs,mmHg,Numeric,90,140
220051,Arterial Blood Pressure diastolic,ABPd,chartevents,Routine Vital Signs,mmHg,Numeric,60,90
220052,Arterial Blood Pressure mean,ABPm,chartevents,Routine Vital Signs,mmHg,Numeric,,

In later exercises, we are interested in the vitals for ICU patients: heart rate (220045), mean non-invasive blood pressure (220181), systolic non-invasive blood pressure (220179), body temperature in Fahrenheit (223761), and respiratory rate (220210). Retrieve a subset of chartevents.csv.gz only containing these items, using the favorite method you learnt in Q2.

Document the steps and show code. Display the number of rows and the first 10 rows of the result tibble.

Q4. AI assistant

Which AI assistants (e.g., GitHub Copilot) do you use when working on this assignment? Which AI model (e.g., GPT-5 mini, GPT-5, Claude Sonnet 4.5) does the AI assistant use? How do you use them? Do you think they help improve your productivity?

Give 5 instances where AI model gave incorrect or misleading answers. You can use screenshots or copy-paste the Q&A.